Positioning method, electronic equipment, computer readable storage medium and chip system

Abstract

The invention is suitable for the technical field of wireless positioning, and provides a positioning method, electronic equipment, a computer readable storage medium and a chip system.The method comprises the steps that channel state information is extracted, and sparse points and residual points of second equipment are determined; calculating the information corresponding to the sparse points according to a preset algorithm to obtain first position information, and calculating the information corresponding to the residual points through a pre-trained neural network to obtain second position information; and determining actual position information of the second equipment according to the first position information and the second position information. According to the method provided by the embodiment of the invention, the information of the residual point in the one-dimensional or multi-dimensional frequency spectrum of the channel state information is acquired, and the second device is positioned again through the trained neural network in combination with the information of the residual point to obtain the second position information, so that the first position information can be corrected through the second position information; and the accuracy of positioning the second equipment can be improved.

Description

Positioning method, electronic device, computer-readable storage medium, and chip system Technical Field

[0001] The present application relates to the field of wireless positioning technology, and in particular to a positioning method, electronic equipment, computer-readable storage medium, and chip system. Background Art

[0002] With the continuous development of wireless communication technology, wireless signals can be used in wireless positioning systems on the basis of being used for data communication to locate the sending or receiving end of the wireless positioning system.

[0003] For example, a wireless positioning system can collect channel state information (CSI) corresponding to the channel between the transmitter and receiver, and then use simultaneous localization and mapping (SLAM) technology to process the CSI to obtain the location of the transmitter or receiver.

[0004] However, in practical applications, some objects (such as buildings and furniture) have irregular geometric shapes and electromagnetic properties. It is difficult for SLAM to obtain the characteristics of these objects based on CSI information, which affects the positioning of the transmitter or receiver.

[0005] Summary of the Invention

[0006] The present application provides a positioning method, electronic device, computer-readable storage medium and chip system, which solve the problem of inaccurate positioning of a transmitter or receiver in the prior art.

[0007] To achieve the above objectives, this application adopts the following technical solutions:

[0008] In a first aspect, a positioning method is provided, applied to any first device in a wireless communication system, the wireless communication system comprising: a plurality of first devices and second devices, the second device being wirelessly connected to each first device, and any two first devices being wirelessly connected to each other, the method comprising:

[0009] extracting channel state information of a communication channel between the first device and the second device, and determining a sparse point and a residual point of the second device;

[0010] Calculating information corresponding to the sparse points according to a preset algorithm to obtain first position information, and calculating information corresponding to the residual points using a pre-trained neural network to obtain second position information, where both the first position information and the second position information are used to indicate the position of the second device;

[0011] The actual location information of the second device is determined according to the first location information and the second location information.

[0012] In a first possible implementation manner of the first aspect, before extracting the channel state information of the communication channel between the first device and the second device and determining the sparse points and residual points of the second device, the method further includes:

[0013] receiving a reference signal sent by the second device;

[0014] The channel state information is determined according to the reference signal.

[0015] In a second possible implementation manner of the first aspect, calculating the information corresponding to the residual point using a pre-trained neural network to obtain the second position information includes:

[0016] Inputting information corresponding to the residual point into the neural network;

[0017] The information corresponding to the residual point is calculated according to the pre-trained algorithm and pre-trained parameters in the neural network to obtain the second position information.

[0018] In a third possible implementation manner of the first aspect, calculating the information corresponding to the sparse point according to a preset algorithm to obtain the first position information includes:

[0019] Associating the sparse points with historical feature points based on information corresponding to the sparse points, where the historical feature points are the extracted sparse points of the second device;

[0020] The first position information is obtained by calculating information corresponding to the sparse points through a preset algorithm in combination with historical feature points associated with the sparse points.

[0021] Based on any of the foregoing possible implementations of the first aspect, in a fourth possible implementation of the first aspect, the method further includes:

[0022] receiving the sparse points and residual points of the second device sent by another second device;

[0023] The calculating the information corresponding to the sparse points according to a preset algorithm to obtain the first position information includes:

[0024] Calculate, according to a preset algorithm, the information corresponding to the sparse point and the information corresponding to the sparse point sent by other second devices to obtain the first position information;

[0025] The calculating the information corresponding to the residual point by the pre-trained neural network to obtain the second position information includes:

[0026] The information corresponding to the residual point and the information corresponding to the residual point sent by other second devices are calculated through a pre-trained neural network to obtain the second position information.

[0027] Based on any one of the foregoing possible implementation manners of the first aspect, in a fifth possible implementation manner of the first aspect, extracting channel state information of the communication channel between the first device and the second device to obtain sparse points and residual points includes:

[0028] Extracting the channel state information to obtain a one-dimensional or multi-dimensional spectrum;

[0029] Each of the frequency spectra is detected to determine sparse points and residual points in each of the frequency spectra.

[0030] Based on any of the foregoing possible implementation manners of the first aspect, in a sixth possible implementation manner of the first aspect, the method further includes:

[0031] Based on the second position information, the neural network is optimized through the loss function of the neural network in combination with the first position information.

[0032] In summary, the positioning method provided by the embodiment of the present application collects the CSI of the communication channel between the first device and the second device, and extracts the CSI to obtain the information corresponding to the sparse points and residual points in the one-dimensional or multi-dimensional spectrum of the CSI, and then calculates the information of the associated sparse points through the SLAM algorithm to obtain the first position information, and inputs the information corresponding to the residual points into a pre-trained neural network, and obtains the second position information through the output of the neural network. Then, based on the first position information and the second position information, the actual position information of the second device is obtained by fusion calculation. By extracting the CSI, the information of the residual points in the one-dimensional or multi-dimensional spectrum of the CSI is obtained, and the second device is positioned again through the trained neural network combined with the information of the residual points to obtain the second position information, so that the first position information can be corrected by the second position information, thereby improving the accuracy of positioning the second device.

[0033] Moreover, the above-mentioned neural network is based on the spatial consistency characteristics of each residual point and combines the mutual information included in the information of each residual point to generate a message format field that is convenient for describing multiple feature combinations through a disentangled representation, thereby improving the accuracy of the second position information and expanding the feature set learned by the neural network.

[0034] In addition, the second device is positioned through multiple first devices, and each first device can interact with the location information of the second device during the positioning process, so that the second device can be positioned through more location information, thereby improving the accuracy of positioning the second device.

[0035] In a second aspect, a positioning apparatus is provided, applied to any first device of a wireless communication system, the wireless communication system comprising: a plurality of the first devices and a second device, the second device being wirelessly connected to each of the first devices, and any two of the first devices being wirelessly connected to each other, the apparatus comprising:

[0036] an extraction module, configured to extract channel state information of the communication channel between the first device and the second device, and determine a sparse point and a residual point of the second device;

[0037] a calculation module, configured to calculate the information corresponding to the sparse points according to a preset algorithm to obtain first position information, and calculate the information corresponding to the residual points using a pre-trained neural network to obtain second position information, wherein both the first position information and the second position information are used to indicate the position of the second device;

[0038] The first determining module is configured to determine actual location information of the second device based on the first location information and the second location information.

[0039] In a first possible implementation manner of the second aspect, the apparatus further includes:

[0040] A first receiving module, configured to receive a reference signal sent by the second device;

[0041] The second determining module is configured to determine the channel state information according to the reference signal.

[0042] In a second possible implementation of the second aspect, the calculation module is specifically used to input the information corresponding to the residual point into the neural network; according to the pre-trained algorithm and pre-trained parameters in the neural network, the information corresponding to the residual point is calculated to obtain the second position information.

[0043] In a third possible implementation of the second aspect, the calculation module is further specifically used to associate the sparse points with historical feature points based on the information corresponding to the sparse points, where the historical feature points are the sparse points of the second device that have been extracted; and through a pre-set algorithm, combined with the historical feature points associated with the sparse points, the information corresponding to the sparse points is calculated to obtain the first position information.

[0044] Based on any of the foregoing possible implementations of the second aspect, in a fourth possible implementation of the first aspect, the apparatus further includes:

[0045] A second receiving module, configured to receive the sparse points and residual points of the second device sent by another second device;

[0046] The calculation module is further specifically configured to calculate, according to a preset algorithm, the information corresponding to the sparse point and the information corresponding to the sparse point sent by other second devices, to obtain the first position information;

[0047] The calculation module is further specifically used to calculate the information corresponding to the residual point and the information corresponding to the residual point sent by other second devices through a pre-trained neural network to obtain the second position information.

[0048] Based on any one of the above-mentioned possible implementations of the second aspect, in a fifth possible implementation of the first aspect, the extraction module is specifically used to extract the channel state information to obtain a one-dimensional or multi-dimensional spectrum; and detect each of the spectra to determine the sparse points and residual points in each of the spectra.

[0049] Based on any of the foregoing possible implementation manners of the second aspect, in a sixth possible implementation manner of the first aspect, the apparatus further includes:

[0050] An optimization module is used to optimize the neural network based on the second position information through the loss function of the neural network in combination with the first position information.

[0051] In a third aspect, an electronic device is provided, comprising: a processor, wherein the processor is configured to run a computer program stored in a memory, so that the electronic device implements the positioning method as described in any one of the first aspects.

[0052] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the positioning method as described in any one of the first aspects is implemented.

[0053] In a fifth aspect, a chip system is provided, comprising a memory and a processor, wherein the processor executes a computer program stored in the memory to implement the positioning method as described in any one of the first aspects.

[0054] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] FIG1 is a waveform diagram of a channel impulse response provided by an embodiment of the present application;

[0056] FIG2A is a system schematic diagram of a wireless communication system provided in an embodiment of the present application;

[0057] FIG2B is a system diagram of another wireless communication system provided in an embodiment of the present application;

[0058] FIG3A is a schematic flow chart of a positioning method provided in an embodiment of the present application;

[0059] FIG3B is a schematic diagram of a framework in which a first device positions a second device according to an embodiment of the present application;

[0060] FIG4 is a schematic diagram of another framework of a first device positioning a second device provided in an embodiment of the present application;

[0061] FIG5 is a structural block diagram of a positioning device provided in an embodiment of the present application;

[0062] FIG6 is a schematic structural diagram of a terminal device provided by the present application;

[0063] FIG7 is a schematic diagram of the structure of a network device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are provided to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of the well-known SLAM algorithm, the method for extracting CSI to obtain sparse points and residual points, the Bayesian fusion algorithm, the terminal device, and the base station are omitted to prevent unnecessary details from obstructing the description of the present application.

[0065] The terms used in the following embodiments are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "said," "above," and "the" are intended to also include expressions such as "one or more," unless the context clearly indicates otherwise.

[0066] With the continuous development of wireless communication technology, on the basis of data communication through wireless signals, positioning can also be performed based on wireless signals, thereby locating the transmitter that transmits the wireless signal, or the receiver that receives the wireless signal, and determining the location of the transmitter or receiver.

[0067] Ultra-wideband (UWB) systems can be used for positioning. Because UWB systems significantly increase the channel bandwidth, their resolution can be improved, enabling more accurate positioning. Furthermore, UWB systems can incorporate oversampling technology to improve the effective signal-to-noise ratio at the receiver.

[0068] Specifically, the UWB system may include multiple UWB base stations. For each UWB base station, the UWB base station can obtain the corresponding channel impulse response (CIR) for the device to be located, and based on the obtained CIR, determine the rising edge of the first peak in the CIR through the leading edge detection (LED) algorithm, determine the first path delay, and multiply it by the speed of light to calculate the first path propagation distance. Then, based on the location of each UWB base station and the first path propagation distance between each UWB base station and the device to be located, the location of the device to be located can be determined.

[0069] However, referring to Figure 1, which is a waveform diagram of a channel impulse response provided in an embodiment of the present application, the figure shows the noise-low intensity determined according to the LED algorithm and the preset first-path rising slope signal strength threshold. Combining the waveform corresponding to the channel impulse response, it can be determined that the peak value of peak A in the channel impulse response is greater than the first-path rising slope signal strength threshold, while the peak value of peak B, while greater than the noise-low intensity, is less than the first-path rising slope signal strength threshold.

[0070] Based on the noise intensity and the first path rising slope signal strength threshold, the distance corresponding to the horizontal axis value 0 where the peak A is located can be considered as the line of sight path (LoS path). When the amplitude of the peak A is greater than the first path rising slope signal strength threshold, the delay corresponding to the horizontal axis position at this time can be determined as the first path delay, so that the LoS path distance can be determined based on the first path delay.

[0071] However, compared with peak A, the peak value of peak B is smaller than the first path rising slope signal strength threshold, indicating that peak B belongs to the NLoS path. At this time, the energy of the first path is severely attenuated, and accurate positioning cannot be performed based on the first path delay.

[0072] For example, in a 500 MHz bandwidth UWB system with a time-domain sampling interval of 0.2 nanoseconds (ns), typical multipath resolution can reach sub-meter levels. However, in real-world environments, many objects can be as small as decimeters or even smaller. UWB systems are unable to distinguish the characteristics of these objects, resulting in unsuccessful multipath resolution or, due to multipath aliasing, inaccurate parameter estimation.

[0073] Therefore, based on the problems that occur in the UWB system, the transmitter or receiver can be located based on the positioning technology of wireless multipath simultaneous localization and mapping (SLAM).

[0074] Specifically, the transmitter or receiver can first obtain CSI and extract a one-dimensional or multi-dimensional spectrum (such as at least one of a delay spectrum, an angular spectrum, and a Doppler spectrum) based on the CSI. Feature point detection can then be performed on the one-dimensional or multi-dimensional spectrum to determine the sparse points in the one-dimensional or multi-dimensional spectrum. The sparse points in each feature point can then be associated with each marked feature point, and then calculated using a SLAM algorithm to obtain the position information of the transmitter or receiver. When the line of sight is blocked, the observed feature points can be associated and matched with the marked feature points to achieve the positioning of the transmitter or receiver.

[0075] Furthermore, positioning can be performed using CSI collected by multiple transmitters or receivers. Because the multiple transmitters or receivers are located at different locations in space, the CSI collected by each transmitter or receiver has spatial diversity, significantly reducing the probability of systematic degradation of positioning accuracy at specific locations and directions. Furthermore, because the CSI collected by multiple transmitters or receivers has a certain degree of repeatability, positioning accuracy can be improved.

[0076] However, in real-world applications, the irregular geometry and electromagnetic properties of buildings, furniture, and other objects make the simple point source model too abstract relative to the actual extended target, leading to model mismatch and suboptimal positioning accuracy. Furthermore, the resolution of wireless systems is insufficient to directly capture the extended target's geometry, making it difficult to obtain its features.

[0077] Therefore, an embodiment of the present application proposes a positioning method in which a first device obtains the CSI corresponding to a second device and processes the CSI using a pre-configured neural network to obtain a feature descriptor describing the second device. Based on the feature descriptor and combined with positioning technology based on wireless multipath SLAM, the position of the second device is determined. By extracting the CSI corresponding to the second device to obtain the feature descriptor of the second device, the positioning accuracy of the second device can be improved based on the feature descriptor, thereby improving the accuracy of positioning the second device.

[0078] The following introduces a wireless communication system involved in a positioning method provided in an embodiment of the present application. See Figure 2A. Figure 2A is a system schematic diagram of a wireless communication system provided in an embodiment of the present application. As shown in Figure 2A, the wireless communication system may include: a first device 210 and a second device 220.

[0079] The above-mentioned wireless communication systems may include: narrowband Internet of Things (NB-IoT) system, global system for mobile communications (GSM), enhanced data rate for GSM evolution (EDGE), wideband code division multiple access (WCDMA), code division multiple access 2000 (CDMA2000), time division-synchronization code division multiple access (TD-SCDMA), long term evolution (LTE) and three major application scenarios of the next generation 5G mobile communication system: enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC) and massive machine type of communication (mMTC). The embodiments of the present application do not limit the wireless communication system.

[0080] A wireless communication system is typically composed of cells, each of which includes a base station (BS). The base station can provide communication services to multiple mobile stations (MS). For example, the first device 210 in the wireless communication system can be a base station, and the second device 220 can be a mobile station; alternatively, the first device 210 in the wireless communication system can be a mobile station, and the second device 220 can be a base station. The embodiments of the present application do not limit the first device 210 and the second device 220.

[0081] Furthermore, a base station may include a baseband unit (BBU) and a remote radio unit (RRU). The BBU and RRU may be placed in different locations. For example, the RRU may be placed in an area with high traffic volume, while the BBU may be placed in a central equipment room. Of course, the BBU and RRU may also be placed in the same area, for example, in the same equipment room, or as different components within the same rack. The present embodiments do not limit the locations of the BBU and RRU.

[0082] In addition, the mobile station can be a terminal device or other wireless communication node (or also include other auxiliary communication and computing nodes), and the embodiments of the present application do not limit the mobile station. In addition, the positioning method provided in the embodiments of the present application can be calculated and implemented by a wireless communication chip in a base station or a mobile station, or by other processing chips in a base station or a mobile station. The embodiments of the present application do not limit the chip that implements the positioning method in the base station or the mobile station.

[0083] The following describes the positioning method and wireless communication system provided in the embodiments of the present application by taking the first device 210 as a base station and the second device 220 as a mobile station as an example.

[0084] Refer to Figure 2B, which is a system schematic diagram of another wireless communication system provided in an embodiment of the present application. As shown in Figure 2B, the wireless communication system may include: a first device A, a first device B and a second device C, and there is a communication channel between the second device C and the first device A and the first device B.

[0085] Accordingly, the second device C can send reference signals (such as reference symbols or long training fields (LTFs)) to the first device A and the first device B respectively. The reference signals reach the first device A and the first device B through multiple paths. Then, the first device A and the first device B can both detect the channel status between the second device C and the first device A and the first device B based on the reference signals to obtain first CSI and second CSI. The first CSI is used to indicate the channel status between the first device A and the second device C, and the second CSI is used to indicate the channel status between the first device A and the second device C.

[0086] The first device A may preprocess the first CSI and then input the preprocessed CSI into a preset neural network to obtain a first feature description word corresponding to the second device C. Similarly, the first device B may also first perform feature extraction on the second CSI to obtain a second feature description word.

[0087] Afterwards, the first device A shares the first feature description word as prior information with the first device B, and the first device B can locate the second device C based on the second feature description word in combination with the first feature description word to determine the position of the second device C.

[0088] Among them, the reference signal can be transmitted in a straight line from the second device C to the first device A and the first device B, or it can be transmitted to the first device A and the first device B through reflection at regular points and / or irregular points, and it can also be transmitted from the second device C to the first device A and the first device B using other paths. The embodiments of the present application do not limit the multiple paths for transmitting the reference signal.

[0089] Moreover, both regular points and irregular points are objects that can reflect reference signals. Regular points can be objects with regular shapes, irregular points can be objects with irregular shapes, or objects with irregular shapes composed of multiple regular points. The embodiments of the present application do not limit regular points and irregular points.

[0090] It should be noted that the prior information may include: identification information of the device under test, estimated position information of the device under test, target features of regular points, and target features and attributes of irregular points.

[0091] The device under test is a device that needs to be positioned by a detection device; the identification information of the device under test is used to indicate the device under test; and the estimated position information of the device under test is the position of the device under test estimated by the detection device.

[0092] For example, the device to be tested may be the second device C, the detection device may be the first device A and the first device B, the identification information of the device to be tested may be the identification information of the second device C, the estimated position information of the device to be tested may be the position information of the second device C estimated by the first device A or the first device B, and the target feature of the regular point may be the position information of the regular point. For example, the position information of the regular point may be coordinate information (such as two-dimensional coordinate information, three-dimensional coordinate information, polar coordinate information, or parameters of one or more dimensions in a specific coordinate system), and it may be described in a distribution manner, such as mean, mean variance, or other distribution representations.

[0093] Moreover, the target features and attributes of irregular points can be multiple channel impulse response (CIR) segments (such as delay, angle and amplitude segments). For example, the target features and attributes of irregular points can include the relative delay of the CIR segment (such as peak A and peak B shown in Figure 1), the number of delays in the CIR segment, and the amplitude and phase of each tap in the CIR segment.

[0094] The following describes a positioning method provided in an embodiment of the present application by taking a first device positioning a second device as an example.

[0095] FIG3A is a schematic flow chart of a positioning method provided in an embodiment of the present application, and FIG3B is a schematic diagram of a framework of a first device positioning a second device provided in an embodiment of the present application. By way of example and not limitation, the method can be applied to the first device of the above-mentioned wireless communication system. Referring to FIG3A and FIG3B , the method includes:

[0096] Step 301: Acquire CSI of a communication channel between a first device and a second device according to a reference signal sent by a second device.

[0097] The reference signal may be a reference symbol or LTF, etc., and the embodiment of the present application does not limit the reference signal.

[0098] In a wireless communication system, a first device can send wireless signals to a second device over a communication channel, and can also receive wireless signals sent by the second device over a communication channel, thereby achieving communication between the first and second devices. In a wireless communication system, a first device can not only communicate with a second device but also locate the second device and determine its position in space.

[0099] Specifically, the second device can send a reference signal to the first device through a communication channel, and the first device can receive the reference signal according to the communication channel, and detect the received reference signal to determine the CSI of the communication channel between the first device and the second device, so that in subsequent steps, the first device can locate the second device based on the obtained CSI.

[0100] Step 302: Extract the CSI to obtain a one-dimensional or multi-dimensional spectrum.

[0101] The one-dimensional or multi-dimensional spectrum may include: delay spectrum, angular spectrum and Doppler spectrum or a combination thereof, such as delay-angular spectrum. The embodiment of the present application does not limit the extracted one-dimensional or multi-dimensional spectrum.

[0102] After obtaining the CSI, the first device can preprocess the CSI to obtain the CIR segment in the CSI so that the second device can be located based on the one-dimensional or multi-dimensional spectrum in the CIR segment in combination with a pre-trained neural network to determine the position of the second device in space.

[0103] Specifically, the first device can first perform CSI denoising using low-pass filtering, then separate the phase and amplitude signals in the CSI, perform linear transformation on the phase signal to eliminate synchronization errors, and normalize the amplitude signal. The first device can reconstruct the normalized amplitude signal with the linearly transformed phase signal and process the reconstructed signal using an inverse fast Fourier transform to obtain multiple CIR segments. From these multiple CIR segments, one-dimensional or multi-dimensional spectra, including delay spectrum, angular spectrum, and Doppler spectrum, can be extracted.

[0104] Step 303: Detect the one-dimensional or multi-dimensional spectrum to determine the sparse points and residual points in the one-dimensional or multi-dimensional spectrum.

[0105] Sparse points are points in the spectrum with energy density greater than a first threshold, while residual points are points in the spectrum with energy density less than a second threshold, where the second threshold is less than the first threshold. In other words, sparse points are points that are relatively sparsely distributed in the spectrum, while residual points are points that are unclear in the spectrum.

[0106] During the process of positioning the second device by the first device, the second device may continue to move, causing the wireless propagation path between the first and second devices to change. Consequently, the CSI of the communication channel between the second and first devices also changes. Accordingly, the sparse points and residual points in the one-dimensional or multi-dimensional spectrum extracted based on the CSI will also change.

[0107] Therefore, the first device can identify sparse points and residual points in the one-dimensional or multi-dimensional spectrum based on the one-dimensional or multi-dimensional spectrum, so that in subsequent steps, the first device can determine the position of the second device based on each sparse point and each residual point.

[0108] Specifically, for each spectrum, the first device can calculate the parameters corresponding to each point in the spectrum (such as frequency, energy density, variance and expected value, etc.) according to a preset calculation formula, and then compare the parameters corresponding to each point based on multiple thresholds such as a preset first threshold and a second threshold to determine the sparse points and residual points included in each spectrum.

[0109] For example, the first device may use a compressed sensing detection method to detect a one-dimensional or multi-dimensional spectrum to obtain sparse points and residual points in the one-dimensional or multi-dimensional spectrum.

[0110] Step 304: For each sparse point, associate the sparse point with a historical feature point.

[0111] Among them, the historical feature points are the sparse points extracted by the first device based on the spectrum before the sparse points are extracted this time. For example, during the process of the first device positioning the second device, the second device can send a reference signal to the first device multiple times. Then, after the first device receives the reference signal for the first time, it can extract the sparse points based on the reference signal; thereafter, the first device can receive the reference signal for the second time and extract the sparse points again. At this time, the sparse points extracted based on the reference signal received for the first time are the historical feature points. Accordingly, the first device can associate the sparse points extracted for the second time with the historical feature points.

[0112] Corresponding to step 303, after determining each sparse point, the first device can identify the location of the second device based on each sparse point. If the first device has already obtained multiple historical feature points before determining the multiple sparse points in step 303, the first device can use data such as the second device's position state and motion state estimation information, as well as information such as the second device's historical state, to make an association decision or calculate the likelihood of association, thereby associating the multiple sparse points determined in step 303 with the multiple historical feature points already obtained.

[0113] Specifically, after the first device extracts each sparse point, for each sparse point, the first device can compare the parameters corresponding to the sparse point (such as distance, angle, speed and other parameters) with the parameters corresponding to each historical feature point, and determine the parameters corresponding to the historical feature point that are the same or similar to the parameters corresponding to the sparse point, so that it can be determined that the historical feature point corresponding to the parameter is the same feature point as the sparse point, and then the sparse point can be associated with the determined historical feature point.

[0114] Similarly, the first device may determine whether each sparse point corresponds to a historical feature point in the above manner, and associate the sparse points corresponding to the historical feature points to obtain a plurality of associated sparse points.

[0115] For example, the historical information of a certain historical feature point includes historical speed and historical position. The historical speed of the second device relative to the first device is (V x = 0 meters per second (m / s), V y =+1m / s), and the coordinates of the historical position are (X=0 meter (m), Y=5m), and the coordinates corresponding to a sparse point are (X=0m, Y=6m), then it can be determined that the sparse point is highly likely associated with the historical feature point.

[0116] Step 305: Based on the SLAM algorithm, calculation is performed according to each sparse point in the one-dimensional or multi-dimensional spectrum to obtain first position information of the second device.

[0117] After the first device has completed associating each sparse point, it can use a pre-set SLAM algorithm to calculate based on each sparse point to obtain the first location information of the second device, so that in subsequent steps, the first device can determine the actual location information of the second device based on the first location information.

[0118] Specifically, the first device can calculate the first position information of the second device based on the information of each sparse point extracted, through the state transfer matrix in the SLAM algorithm, and then combine the preset input control model, control input information and noise.

[0119] Step 306: Input the residual point into the neural network to obtain the second position information of the second device.

[0120] Corresponding to step 304, the first device can not only associate the extracted sparse points to obtain the first position information, but also input the information of the extracted residual points into a pre-set neural network, and calculate the information of the residual points through the neural network to obtain the second position information.

[0121] Specifically, the first device may input a message format field composed of information about each residual point into a pre-set neural network, and use the neural network to determine the characteristic attributes included in each piece of information in the message format field. For each piece of information, the first device may select the two characteristic attributes with the largest mutual information based on the mutual information included in each characteristic attribute for the corresponding residual point, and calculate the second position information of the second device in combination with pre-set parameters.

[0122] For example, the information corresponding to the jth residual point is S j , the neural network is in S j After identification, the characteristic attribute with the maximum mutual information with the jth residual point can be obtained as The characteristic attribute with the second largest mutual information with the jth residual point can be in, Afterwards, the neural network can calculate according to the preset formula and the preset parameters to obtain the second position information of the second device. Among them, d pos Used to indicate the second position information, C length The length of the message format field used to represent the information of multiple residual points, H(S j ) is a preset parameter used to represent the entropy corresponding to the j-th residual point.

[0123] It should be noted that in actual applications, information about multiple residual points can be expressed using different message format fields. That is, the second device can be represented using multiple description methods. For example, a limited description method can be used, where the number of messages in the message format field is described in the first message in the message format field; or a continuous description method can be used, where the number of messages in the message format field is continuously increased.

[0124] For example, if a tray contains multiple metal cups, they can be represented by a message format field consisting of four messages. The first message can describe that this is an elliptically distributed scatterer set (rather than a rectangular distribution), the second message can describe the length of the major axis of the elliptically distributed scatterer set, the third message can describe the length of the minor axis of the elliptically distributed scatterer set, and the fourth message can describe the elliptically distributed scatterer density, or the spatial Poisson arrival rate.

[0125] It should also be noted that in actual applications, due to factors such as noise and interference, the obtained residual points may include false residual points, which do not correspond to actual feature points in the environment where the second device is located. Therefore, when the first device locates the second device using a neural network, the neural network can also identify and generalize false residual points among the multiple residual points to improve the accuracy of the second position information.

[0126] Moreover, in actual applications, due to the weakening of the wireless path strength of the environmental feature points, the first device may encode the residual point information according to the signal strength in a disordered manner (for example, the information of multiple environmental feature points in the environment where the second device is located at different times may be exchanged as a whole, or the order of the four messages in the above example may be disrupted). Therefore, the neural network can also generalize the arrangement order of the information of each residual point, thereby improving the accuracy of the second location information.

[0127] Accordingly, in the process of training the neural network and optimizing the neural network in step 308, the first device can target the second position information, that is, d in the above example. pos , combined with the generalization method for the above-mentioned false residual points and arrangement order, the neural network is trained so that the trained neural network can generalize the false residual points and arrangement order, thereby outputting more accurate second position information.

[0128] Step 307: Determine the actual location information of the second device based on the first location information and the second location information.

[0129] After determining the first location information and the second location information, the first device may further analyze the first location information and the second location information to determine the actual location of the second device, that is, the actual location information of the second device, and complete the positioning of the second device.

[0130] Specifically, the first device can use a pre-set Bayesian function to calculate based on the first position information and the second position information to obtain the probability of the second device being in different spatial positions, then select the spatial position corresponding to the maximum probability, and determine the spatial position as the actual location of the second device, thereby obtaining the actual location information of the second device.

[0131] For example, after obtaining the first location information and the second location information, the first device can update the probability of the second device at different spatial locations based on the first location information and the second location information, so that the updated probability is closer to the actual location of the second device.

[0132] Step 308: Optimize the neural network based on the first position information and the second position information in combination with the loss function of the neural network.

[0133] Corresponding to step 305, after obtaining the first position information, the first device can optimize the neural network based on the first position information and the second position information, so that the neural network can output more accurate second position information, thereby improving the accuracy of positioning the second device.

[0134] Specifically, the first device can input the first position information and the second position information into the localization loss function of the neural network, use the first position information as label data, and use the second position information as output data. By comparing the first position information and the second position information, the parameters in the localization loss function are adjusted to achieve optimization of the neural network.

[0135] It should be noted that the above description uses one first device to locate one second device as an example, but in actual applications, one second device can be located by multiple first devices. For example, corresponding to Figure 2B, one second device can be located by two first devices.

[0136] Referring to Figure 4, Figure 4 is a schematic diagram of a framework in which two first devices (first device A and first device B) locate one second device. Specifically, both the first device A and the first device B can obtain the CSI of the communication channel between them and the second device. The first device B can associate the multiple sparse points extracted in a manner similar to steps 302 to 304, and send the information of the associated sparse points to the first device A. Moreover, the first device B can input the information of the residual points into the neural network in a manner similar to step 306, process the information of the residual points through the encoder of the neural network, obtain intermediate data, and then send the processed intermediate data to the first device A.

[0137] Similarly, first device A may also associate the multiple extracted sparse points in a manner similar to steps 302 to 304, and receive information about the multiple associated sparse points sent by first device B. Thereafter, first device A may employ a method similar to step 305, using a pre-set SLAM algorithm to perform calculations based on the associated sparse point information and the associated sparse point information received from first device B, to obtain first position information generated by combining the sparse point information extracted by the two first devices.

[0138] Moreover, the first device A can also use a method similar to step 306 to input the information of the residual point into the neural network, process the information of the residual point through the encoder of the neural network to obtain intermediate data, and then combine the intermediate data sent by the first device B, and process the two intermediate data through the decoder of the neural network to obtain second position information.

[0139] Afterwards, the first device A may obtain the actual location information of the second device according to the first location information and the second location information in a manner similar to step 307, thereby completing the positioning of the second device.

[0140] It should be noted that the process of the first device A and the first device B positioning the second device is similar to the positioning process of step 301 to step 307, and will not be repeated here.

[0141] In summary, the positioning method provided by the embodiment of the present application collects the CSI of the communication channel between the first device and the second device, and extracts the CSI to obtain the information corresponding to the sparse points and residual points in the one-dimensional or multi-dimensional spectrum of the CSI, and then calculates the information of the associated sparse points through the SLAM algorithm to obtain the first position information, and inputs the information corresponding to the residual points into a pre-trained neural network, and obtains the second position information through the output of the neural network. Then, based on the first position information and the second position information, the actual position information of the second device is obtained by fusion calculation. By extracting the CSI, the information of the residual points in the one-dimensional or multi-dimensional spectrum of the CSI is obtained, and the second device is positioned again through the trained neural network combined with the information of the residual points to obtain the second position information, so that the first position information can be corrected by the second position information, thereby improving the accuracy of positioning the second device.

[0142] Moreover, the above-mentioned neural network is based on the spatial consistency characteristics of each residual point and combines the mutual information included in the information of each residual point to generate a message format field that is convenient for describing multiple feature combinations through a disentangled representation, thereby improving the accuracy of the second position information and expanding the feature set learned by the neural network.

[0143] In addition, the second device is positioned through multiple first devices, and each first device can interact with the location information of the second device during the positioning process, so that the second device can be positioned through more location information, thereby improving the accuracy of positioning the second device.

[0144] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0145] Corresponding to the positioning method described in the above embodiment, FIG5 is a structural block diagram of a positioning device provided in an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0146] Referring to FIG5 , the device comprises:

[0147] An extraction module 501 is configured to extract channel state information of a communication channel between the first device and the second device, and determine a sparse point and a residual point of the second device;

[0148] a calculation module 502, configured to calculate information corresponding to the sparse point according to a preset algorithm to obtain first position information, and to calculate information corresponding to the residual point using a pre-trained neural network to obtain second position information, wherein both the first position information and the second position information are used to indicate the position of the second device;

[0149] The first determining module 503 is configured to determine the actual location information of the second device according to the first location information and the second location information.

[0150] Optionally, the device further includes:

[0151] A first receiving module 504, configured to receive a reference signal sent by the second device;

[0152] The second determining module 505 is configured to determine the channel state information according to the reference signal.

[0153] Optionally, the calculation module 502 is specifically used to input the information corresponding to the residual point into the neural network; calculate the information corresponding to the residual point according to the pre-trained algorithm and pre-trained parameters in the neural network to obtain the second position information.

[0154] Optionally, the calculation module 502 is also specifically used to associate the sparse point with a historical feature point based on the information corresponding to the sparse point, where the historical feature point is the sparse point of the second device that has been extracted; through a pre-set algorithm, combined with the historical feature point associated with the sparse point, the information corresponding to the sparse point is calculated to obtain the first position information.

[0155] Optionally, the device further includes:

[0156] A second receiving module 506 is configured to receive sparse points and residual points of the second device sent by another second device;

[0157] The calculation module 502 is further specifically configured to calculate, according to a preset algorithm, the information corresponding to the sparse point and the information corresponding to the sparse point sent by the other second devices, to obtain the first position information;

[0158] The calculation module 502 is further specifically configured to calculate the information corresponding to the residual point and the information corresponding to the residual point sent by other second devices through a pre-trained neural network to obtain the second position information.

[0159] Optionally, the extraction module 501 is specifically configured to extract the channel state information to obtain a one-dimensional or multi-dimensional spectrum; detect each of the spectrum to determine sparse points and residual points in each of the spectrum.

[0160] Optionally, the device further includes:

[0161] The optimization module 507 is used to optimize the neural network based on the second position information through the loss function of the neural network and the first position information.

[0162] In summary, the embodiment of the present application provides a positioning device, which collects the CSI of the communication channel between the first device and the second device, extracts the CSI, obtains the information corresponding to the sparse points and residual points in the one-dimensional or multi-dimensional spectrum of the CSI, and then calculates the information of the associated sparse points through the SLAM algorithm to obtain the first position information, and inputs the information corresponding to the residual points into a pre-trained neural network, and obtains the second position information through the output of the neural network. Then, based on the first position information and the second position information, the actual position information of the second device is obtained by fusion calculation. By extracting the CSI, the information of the residual points in the one-dimensional or multi-dimensional spectrum of the CSI is obtained, and the second device is positioned again through the trained neural network combined with the information of the residual points to obtain the second position information, so that the first position information can be corrected by the second position information, thereby improving the accuracy of positioning the second device.

[0163] Figure 6 is a schematic diagram of the structure of a terminal device provided in this application. The first device or the second device described above can be configured in the terminal device 600. Alternatively, the first device or the second device itself can be the terminal device 600. In other words, the terminal device 600 can perform the actions performed by the terminal device in the methods corresponding to Figures 3A, 3B, and 4. Optionally, for ease of explanation, Figure 6 only shows the main components of the terminal device. As shown in Figure 6, the terminal device 600 includes a processor, memory, control circuitry, an antenna, and input / output devices.

[0164] The processor is primarily used to process communication protocols and communication data, control the entire terminal device, execute software programs, and process software program data, such as supporting the terminal device in executing the actions described in the embodiment of the method for indicating a transmission precoding matrix. The memory is primarily used to store software programs and data, such as the codebook described in the above embodiment. The control circuit is primarily used to convert baseband signals into radio frequency signals and process radio frequency signals. The control circuit and antenna together are also called a transceiver, which is primarily used to transmit and receive radio frequency signals in the form of electromagnetic waves. Input and output devices, such as touch screens, displays, and keyboards, are primarily used to receive user input and output data to the user.

[0165] When the terminal device is powered on, the processor reads the software program stored in the storage unit, interprets and executes the program's instructions, and processes the program's data. When data needs to be transmitted wirelessly, the processor performs baseband processing on the data to be transmitted and outputs the baseband signal to the RF circuit. The RF circuit then performs RF processing on the baseband signal and transmits it via the antenna as electromagnetic waves. When data is sent to the terminal device, the RF circuit receives the RF signal via the antenna, converts it into a baseband signal, and outputs the baseband signal to the processor, which converts the baseband signal into data and processes it.

[0166] Those skilled in the art will appreciate that, for ease of explanation, FIG6 shows only one memory and processor. In an actual terminal device, multiple processors and memories may exist. The memory may also be referred to as a storage medium or storage device, etc., which is not limited in the present embodiment.

[0167] For example, a processor may include a baseband processor and a central processing unit (CPU). The baseband processor is primarily responsible for processing communication protocols and communication data, while the CPU is primarily responsible for controlling the entire terminal device, executing software programs, and processing data from software programs. The processor in Figure 6 integrates the functions of both the baseband processor and the CPU. Those skilled in the art will appreciate that the baseband processor and the CPU may also be independent processors interconnected via a bus or other technology. Those skilled in the art will appreciate that a terminal device may include multiple baseband processors to accommodate different network standards, multiple CPUs to enhance its processing capabilities, and that the various components of the terminal device may be connected via various buses. The baseband processor may also be referred to as a baseband processing circuit or a baseband processing chip. The CPU may also be referred to as a central processing circuit or a central processing chip. The functionality for processing communication protocols and communication data may be built into the processor or stored as a software program in a storage unit, with the processor executing the software program to implement the baseband processing functionality.

[0168] For example, in the embodiment of the present application, the antenna and control circuit with transceiver functions can be regarded as the transceiver unit 601 of the terminal device 600, and the processor with processing function can be regarded as the processing unit 602 of the terminal device 600. As shown in Figure 6, the terminal device 600 includes a transceiver unit 601 and a processing unit 602. The transceiver unit can also be referred to as a transceiver, a transceiver, a transceiver device, etc. Optionally, the device used to implement the receiving function in the transceiver unit 601 can be regarded as a receiving unit, and the device used to implement the transmitting function in the transceiver unit 601 can be regarded as a transmitting unit, that is, the transceiver unit 601 includes a receiving unit and a transmitting unit. For example, the receiving unit can also be referred to as a receiver, a receiver, a receiving circuit, etc., and the transmitting unit can be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0169] Figure 7 is a structural diagram of a network device provided in an embodiment of the present application, which can be used to implement the functions of the network device in the above method. The network device 700 includes one or more radio frequency units, such as a remote radio unit (RRU) 701 and one or more baseband units (BBU) (also called digital units, DU) 702. The RRU 701 can be called a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, etc., and may include at least one antenna and a radio frequency unit. The RRU 701 part is mainly used for receiving and transmitting radio frequency signals and converting radio frequency signals into baseband signals, for example, for sending signaling messages in the above embodiment to terminal devices. The BBU 702 part is mainly used for baseband processing, controlling the base station, etc. The RRU 701 and BBU 702 can be physically set together or physically separated, that is, a distributed base station.

[0170] The BBU 702 is the control center of the base station, which can also be called a processing unit. It is mainly used to perform baseband processing functions such as channel coding, multiplexing, modulation, spread spectrum, etc. For example, the BBU (processing unit) 702 can be used to control the base station to execute the operation process of the network device in the above method embodiment.

[0171] In one example, the BBU 702 may be composed of one or more single boards, and the multiple single boards may jointly support a wireless access network of a single access standard (such as an LTE system or a 5G system), or may respectively support wireless access networks of different access standards. The BBU 702 also includes a memory and a processor. The memory is used to store necessary instructions and data. For example, the memory stores the codebook in the above embodiment, etc. The processor is used to control the base station to perform necessary actions, such as controlling the base station to execute the operation process of the network device in the above method embodiment. The memory and processor can serve one or more single boards. That is, a memory and a processor can be set separately on each single board. Alternatively, multiple single boards may share the same memory and processor. In addition, necessary circuits can also be set on each single board.

[0172] In one possible implementation, with the development of system-on-chip (SoC) technology, all or part of the functions of parts 702 and 701 can be implemented using SoC technology. For example, they can be implemented using a base station function chip that integrates a processor, memory, antenna interface, and other components. Programs for base station-related functions are stored in the memory, and the processor executes the programs to implement the base station-related functions. Optionally, the base station function chip can also read memory external to the chip to implement the base station-related functions.

[0173] It should be understood that the structure of the network device illustrated in FIG7 is only one possible form and should not constitute any limitation to the embodiments of the present application. The present application does not exclude the possibility of other forms of base station structures that may appear in the future.

[0174] It should be understood that in the embodiments of the present application, the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0175] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0176] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0177] An embodiment of the present application also provides a communication system, which includes: the above-mentioned terminal device and the above-mentioned network device.

[0178] The present invention also provides a computer-readable medium for storing computer program code, wherein the computer program includes instructions for executing the positioning method of the present invention in the methods corresponding to Figures 3A, 3B, and 4. The computer-readable medium can be a read-only memory (ROM) or a random access memory (RAM), which is not limited in the present invention.

[0179] The present application also provides a computer program product, which includes instructions. When the instructions are executed, the terminal device performs terminal device operations corresponding to the above method, or the network device performs network device operations corresponding to the above method.

[0180] The present invention also provides a system-on-chip (SoC) comprising a processing unit and a communication unit. The processing unit may be, for example, a processor, and the communication unit may be, for example, an input / output interface, a pin, or a circuit. The processing unit may execute computer instructions to cause the chip within the communication device to perform any of the positioning methods provided in the embodiments of the present invention.

[0181] Optionally, any one of the communication devices provided in the above embodiments of the present application may include the system chip.

[0182] Optionally, the computer instructions are stored in a storage unit.

[0183] Optionally, the storage unit is a storage unit within the chip, such as a register, cache, etc. The storage unit may also be a storage unit located outside the chip within the terminal, such as a ROM or other type of static storage device that can store static information and instructions, RAM, etc. The processor mentioned in any of the above may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the above-mentioned method for main system information transmission. The processing unit and the storage unit may be decoupled and respectively provided on different physical devices, and connected by wired or wireless means to implement the respective functions of the processing unit and the storage unit, so as to support the system chip to implement the various functions in the above-mentioned embodiments. Alternatively, the processing unit and the memory may also be coupled on the same device.

[0184] It is understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0185] The terms "system" and "network" are often used interchangeably in this document. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates an "or" relationship between the related objects.

[0186] The terms "uplink" and "downlink" appearing in this application are used to describe the direction of data / information transmission in specific scenarios. For example, the "uplink" direction generally refers to the direction of data / information transmission from the terminal to the network side, or the direction of data / information transmission from a distributed unit to a centralized unit. The "downlink" direction generally refers to the direction of data / information transmission from the network side to the terminal, or the direction of data / information transmission from a centralized unit to a distributed unit. It can be understood that "uplink" and "downlink" are only used to describe the transmission direction of data / information, and the specific starting and ending devices of the data / information transmission are not limited.

[0187] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical solutions.

[0188] Those skilled in the art will appreciate that the methods in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instruction is loaded and executed on a computer, the process or function described in the embodiments of the present application is executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program or instruction can be stored in a computer-readable storage medium or transmitted via the computer-readable storage medium. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server that integrates one or more available media.

[0189] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0190] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the unit is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0191] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0192] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0193] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage media include: USB flash drives, mobile hard drives, read-only memories (ROMs), and random access memories.

[0194] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A positioning method, characterized in that: A method for applying to any first device in a wireless communication system, the wireless communication system comprising: a plurality of the first devices and a second device, wherein the second device is wirelessly connected to each of the first devices, and any two of the first devices are wirelessly connected to each other, comprising: extracting channel state information of a communication channel between the first device and the second device, and determining a sparse point and a residual point of the second device; Calculating information corresponding to the sparse points according to a preset algorithm to obtain first position information, and calculating information corresponding to the residual points using a pre-trained neural network to obtain second position information, where both the first position information and the second position information are used to indicate the position of the second device; The actual location information of the second device is determined according to the first location information and the second location information.

2. The method according to claim 1, characterized in that Before extracting the channel state information of the communication channel between the first device and the second device and determining the sparse points and residual points of the second device, the method further includes: receiving a reference signal sent by the second device; The channel state information is determined according to the reference signal.

3. The method according to claim 1, characterized in that The calculating the information corresponding to the residual point by the pre-trained neural network to obtain the second position information includes: Inputting information corresponding to the residual point into the neural network; The information corresponding to the residual point is calculated according to the pre-trained algorithm and pre-trained parameters in the neural network to obtain the second position information.

4. The method according to claim 1, wherein The calculating the information corresponding to the sparse points according to a preset algorithm to obtain the first position information includes: Associating the sparse points with historical feature points based on information corresponding to the sparse points, where the historical feature points are the extracted sparse points of the second device; The first position information is obtained by calculating information corresponding to the sparse points through a preset algorithm in combination with historical feature points associated with the sparse points.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: receiving the sparse points and residual points of the second device sent by another second device; The calculating the information corresponding to the sparse points according to a preset algorithm to obtain the first position information includes: Calculate, according to a preset algorithm, the information corresponding to the sparse point and the information corresponding to the sparse point sent by other second devices to obtain the first position information; The calculating the information corresponding to the residual point by the pre-trained neural network to obtain the second position information includes: The information corresponding to the residual point and the information corresponding to the residual point sent by other second devices are calculated through a pre-trained neural network to obtain the second position information.

6. The method according to any one of claims 1 to 5, characterized in that: The extracting channel state information of the communication channel between the first device and the second device to obtain sparse points and residual points includes: Extracting the channel state information to obtain a one-dimensional or multi-dimensional spectrum; Each of the frequency spectra is detected to determine sparse points and residual points in each of the frequency spectra.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Based on the second position information, the neural network is optimized through the loss function of the neural network in combination with the first position information.

8. An electronic device, characterized in that: include: A processor, wherein the processor is configured to run a computer program stored in a memory so as to enable the electronic device to implement the positioning method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the positioning method according to any one of claims 1 to 7 is implemented.

10. A chip system, characterized in that: The chip system includes a memory and a processor, and the processor executes a computer program stored in the memory to implement the positioning method according to any one of claims 1 to 7.

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